using Mscc.GenerativeAI;

var model = new GenerativeModel();

using Mscc.GenerativeAI;
// Google AI with an API key
var googleAI = new GoogleAI(apiKey: "your API key");
var model = googleAI.GenerativeModel(model: Model.Gemini15Pro);

// Original approach, still valid.
// var model = new GenerativeModel(apiKey: "your API key", model: Model.GeminiPro);

using Mscc.GenerativeAI;
// Google AI with OAuth. Use `gcloud auth application-default print-access-token` to get the access token.
var model = new GenerativeModel(model: Model.GeminiPro);
model.AccessToken = accessToken;

using Mscc.GenerativeAI;
// Vertex AI with OAuth. Use `gcloud auth application-default print-access-token` to get the access token.
var vertex = new VertexAI(projectId: projectId, region: region);
var model = vertex.GenerativeModel(model: Model.Gemini15Pro);
model.AccessToken = accessToken;

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var prompt = "Write a story about a magic backpack.";

var model = new GenerativeModel(apiKey: apiKey, model: Model.GeminiPro);

var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);

using Mscc.GenerativeAI;

var projectId = "your_google_project_id"; // the ID of a project, not its name.
var region = "us-central1";     // see documentation for available regions.
var accessToken = "your_access_token";      // use `gcloud auth application-default print-access-token` to get it.
var prompt = "Write a story about a magic backpack.";

var vertex = new VertexAI(projectId: projectId, region: region);
var model = vertex.GenerativeModel(model: Model.Gemini15Pro);
model.AccessToken = accessToken;

var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);

var apiKey = "your_api_key";
var systemInstruction = new Content("You are a friendly pirate. Speak like one.");
var prompt = "Good morning! How are you?";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15ProLatest, systemInstruction: systemInstruction);
var request = new GenerateContentRequest(prompt);

var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);

var apiKey = "your_api_key";
var prompt = "Who won Wimbledon this year?";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel("gemini-1.5-pro-002",
    tools: [new Tool { GoogleSearchRetrieval = 
        new(DynamicRetrievalConfigMode.ModeUnspecified, 0.06f) }]);

var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var prompt = "Parse the time and city from the airport board shown in this image into a list, in Markdown";
var model = new GenerativeModel(apiKey: apiKey, model: Model.GeminiVisionPro);
var request = new GenerateContentRequest(prompt);
await request.AddMedia("https://raw.githubusercontent.com/mscraftsman/generative-ai/refs/heads/main/tests/Mscc.GenerativeAI/payload/timetable.png");

var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var model = new GenerativeModel(apiKey: apiKey);    // using default model: gemini-1.5-pro
var chat = model.StartChat();   // optionally pass a previous history in the constructor.

// Instead of discarding you could also use the response and access `response.Text`.
_ = await chat.SendMessage("Hello, fancy brainstorming about IT?");
_ = await chat.SendMessage("In one sentence, explain how a computer works to a young child.");
_ = await chat.SendMessage("Okay, how about a more detailed explanation to a high schooler?");
_ = await chat.SendMessage("Lastly, give a thorough definition for a CS graduate.");

// A chat session keeps every response in its history.
chat.History.ForEach(c => Console.WriteLine($"{c.Role}: {c.Text}"));

// Last request/response pair can be removed from the history.
var latest = chat.Rewind();
Console.WriteLine($"{latest.Sent} - {latest.Received}");

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var prompt = "Make a short story from the media resources. The media resources are:";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15Pro);

// Upload your large image(s).
// Instead of discarding you could also use the response and access `response.Text`.
var filePath = Path.Combine(Environment.CurrentDirectory, "verylarge.png");
var displayName = "My very large image";
_ = await model.UploadMedia(filePath, displayName);

// Create the prompt with references to File API resources.
var request = new GenerateContentRequest(prompt);
var files = await model.ListFiles();
foreach (var file in files.Where(x => x.MimeType.StartsWith("image/")))
{
    Console.WriteLine($"File: {file.Name}");
    request.AddMedia(file);
}
var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);

using Mscc.GenerativeAI;

var projectId = "your_google_project_id"; // the ID of a project, not its name.
var accessToken = "your_access_token";      // use `gcloud auth application-default print-access-token` to get it.
var model = new GenerativeModel(apiKey: null, model: Model.Gemini10Pro001)
{
    AccessToken = accessToken, ProjectId = projectId
};
var parameters = new HyperParameters() { BatchSize = 2, LearningRate = 0.001f, EpochCount = 3 };
var dataset = new List<TuningExample>
{    
    new() { TextInput = "1", Output = "2" },
    new() { TextInput = "3", Output = "4" },
    new() { TextInput = "-3", Output = "-2" },
    new() { TextInput = "twenty two", Output = "twenty three" },
    new() { TextInput = "two hundred", Output = "two hundred one" },
    new() { TextInput = "ninety nine", Output = "one hundred" },
    new() { TextInput = "8", Output = "9" },
    new() { TextInput = "-98", Output = "-97" },
    new() { TextInput = "1,000", Output = "1,001" },
    new() { TextInput = "thirteen", Output = "fourteen" },
    new() { TextInput = "seven", Output = "eight" },
};
var request = new CreateTunedModelRequest(Model.Gemini10Pro001, 
    "Simply autogenerated Test model",
    dataset,
    parameters);

var response = await model.CreateTunedModel(request);
Console.WriteLine($"Name: {response.Name}");
Console.WriteLine($"Model: {response.Metadata.TunedModel} (Steps: {response.Metadata.TotalSteps})");

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